Sold by: John Snow Labs
Deployed on AWS
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Model with advanced clinical decision-support system built for structured medical reasoning rather than simple knowledge retrieval.
It analyzes symptoms, diagnostics, and longitudinal patient histories to guide complex diagnostic and treatment decisions in line with established clinical guidelines.
With multimodal vision capabilities, it can also interpret medical images and visual documents alongside text, broadening the range of clinical inputs.
Overview
This model is an advanced clinical decision-support system built for structured medical reasoning rather than simple knowledge retrieval.
It analyzes symptoms, diagnostics, and longitudinal patient histories to guide complex diagnostic and treatment decisions in line with established clinical guidelines.
With multimodal vision capabilities, it can also interpret medical images and visual documents alongside text, broadening the range of clinical inputs it can reason over. Now grounded in curated, specialty-specific medical knowledge bases, it anchors conclusions to vetted clinical evidence for greater factual reliability. It delivers transparent, step-aware reasoning, weighs competing hypotheses, and communicates uncertainty to support risk-aware clinical judgment.
IMPORTANT USAGE INFORMATION:
After subscribing to this product and creating a SageMaker endpoint, billing occurs on an HOURLY BASIS for as long as the endpoint is running.
-Charges apply even if the endpoint is idle and not actively processing requests.
-To stop charges, you MUST DELETE the endpoint in your SageMaker console.
-Simply stopping requests will NOT stop billing.
This ensures you are only billed for the time you actively use the service.
Highlights
- ->Achieves 93.0% average across OpenMed benchmarks; ->Medical genetics: 99%; professional medicine: 98%; ->Clinical knowledge comprehension: 94%; college biology mastery: 99%; ->College medicine: 90.5%; anatomy: 92%; ->Achieves 93.5% on MedQA and 78% on PubMedQA; ->Safety & reliability: 94.5% on hallucination detection, inappropriate-content , refusal of unethical/illegal requests; -> Fairness & bias: 98% pass rate on name bias, 93% on racial bias , and 91.67% on anchoring-bias resistance
- Real-Time Inference ->Instance Type: ml.p4d.24xlarge : Tokens per Second during real-time inference: up to 280 tokens per second ->Instance Type: ml.p5.48xlarge : Tokens per Second during real-time inference: up to 380 tokens per second Batch: ->instance Type: ml.g5.48xlarge: Tokens per Second during batch : up to 20 tokens per second
Details
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Deployed on AWS
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Pricing
Free trial
Try this product free for 15 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.48xlarge Inference (Batch) Recommended | Model inference on the ml.g5.48xlarge instance type, batch mode | $19.96 |
ml.p4d.24xlarge Inference (Real-Time) Recommended | Model inference on the ml.p4d.24xlarge instance type, real-time mode | $19.96 |
ml.g4dn.12xlarge Inference (Batch) | Model inference on the ml.g4dn.12xlarge instance type, batch mode | $19.96 |
ml.g5.2xlarge Inference (Batch) | Model inference on the ml.g5.2xlarge instance type, batch mode | $19.96 |
ml.g4dn.12xlarge Inference (Real-Time) | Model inference on the ml.g4dn.12xlarge instance type, real-time mode | $19.96 |
ml.g6.48xlarge Inference (Real-Time) | Model inference on the ml.g6.48xlarge instance type, real-time mode | $19.96 |
ml.g5.2xlarge Inference (Real-Time) | Model inference on the ml.g5.2xlarge instance type, real-time mode | $19.96 |
ml.p5.48xlarge Inference (Real-Time) | Model inference on the ml.p5.48xlarge instance type, real-time mode | $19.96 |
Vendor refund policy
No refunds are possible.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Major advancement in AI-powered clinical decision support, emphasizing structured medical reasoning rather than simple knowledge retrieval.
Additional details
Inputs
- Summary
{ "messages": [ {"role": "system", "content": "You are an expert medical AI assistant."}, {"role": "user", "content": ""} ], "stream": false }
See field explanantion details in the documentation
- Input MIME type
- application/json
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/MedRouter/inputs/realtime
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/MedRouter/inputs/batch
Support
Vendor support
For any assistance, please reach out to support@johnsnowlabs.com .
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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